Real-time field mapping for autonomous agricultural platform

ABSTRACT

A method of using an unmanned agricultural robot to generate an anticipatory geospatial data map of the positions of annual crop rows planted within a perimeter of an agricultural field, the method including the step of creating a geospatial data map of an agricultural field by plotting actual annual crop row positions in a portion of the geospatial data map that corresponds to a starting point observation window, and filling in a remainder of the geospatial data map with anticipated annual crop row positions corresponding to the annual crop rows outside of the starting point observation window, and refining the geospatial data map by replacing the anticipated annual crop row positions with measured actual annual crop row positions when an unexpected obstacle is encountered.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 15/427,265filed Feb. 8, 2017, which claims the benefit of U.S. ProvisionalApplication 62/293,070, filed Feb. 9, 2016, each of which is herebyfully incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to unmanned agriculturalrobots. More particularly, the present disclosure relates to thegeneration of a geospatial data map through the use of a combination ofmeasured, actual crop row positions and anticipated crop row positions.

BACKGROUND

Specific robotic solutions are currently being developed to aid farmersin the growth of annual row crops, such as corn. Specific solutionsinclude improvements in tailoring the amount of fertilizer added to aparticular area of an agricultural field to fit the needs of the cropswithin that area, fertilizing crops that have grown to a height whereuse of conventional fertilization equipment would be impractical,seeding a second cover crop while a first crop is still growing ormature and still on the field and/or the collection of various data tomaximize the output of an agricultural field. Several examples ofunmanned agricultural robots are disclosed in U.S. Pat. Nos. 9,288,938;9,392,743; and 9,265,187, the contents of which are incorporated byreference herein.

Unmanned agricultural robots that operate autonomously in agriculturalsettings require geospatial data as a basis for their operation. In atypical application, a field perimeter defines an absolute boundaryacross which an unmanned agricultural robot is restricted from crossingfor safety reasons. Within the perimeter, various more refined data maybe required to facilitate operation. For example, high-resolution dataon the actual crop row positions, either previously collected orgenerated on-the-fly, may be necessary to prevent crop damage by theunmanned agricultural robot.

In cases where the unmanned agricultural robot is expected to navigatebetween two adjacent crop rows, the geospatial data is often referred toas an “as-planted map,” which is typically created using the GPS-based“precision planting” system on the tractor used for planting operations.However, not all fields are planted with GPS-based systems, and thegeospatial data for those fields that are planted with GPS-based systemscan be of variable accuracy.

In a typical operation of an unmanned agricultural robot, the geospatialdata of the crop location is combined with sensors onboard the unmannedrobotic platform for fine-scale navigation. That is, one or more sensorsdetermine the proximity of the unmanned agricultural robot side-to-sidebetween the crop rows, thereby providing feedback to the unmannedagricultural robot's control system, which in turn continually adjuststhe orientation of the unmanned agricultural robot relative to the croprows.

Assuming that there is a high-quality as-planted map for a field, aswell as onboard sensors for understanding the precise location of croprows, there can still be unexpected situations that would impact thenavigation of an unmanned agricultural robot, such as mis-planted rowsor weeds. Thus, even the best current precision planting technology maynot be sufficient for fully enabling operation of the unmannedagricultural robot on agricultural fields.

What is needed for robust navigation of unmanned agricultural robots onagricultural fields is an on-board system that can learn essentialdetails of the field in real time. Such a system would be flexible inthe sense that it could map the entire field with minimal pre-existinginformation, taking into account challenges such as mis-planted rows andpatches of weeds.

SUMMARY OF THE DISCLOSURE

Embodiments of the present disclosure meet the need for a robustreal-time mapping solution to support navigation of unmannedagricultural robots that carry out various in-season management tasks onagricultural fields. Examples of such management tasks includecollecting plant and soil data, weed removal, fertilizer application,and seeding of cover crops between rows of a mature crop plants, likecorn.

One embodiment of the present disclosure provides a method of using anunmanned agricultural robot to generate an anticipatory geospatial datamap of the positions of annual crop rows planted within a perimeter ofan agricultural field. The method includes delivering an unmannedagricultural robot, programmed with a self-direction program, to theagricultural field. The unmanned agricultural robot can be positioned ata starting point on the agricultural field. One or more aerial mappingsensors can be deployed at a height above the annual crop rows, so as toenable the one or more aerial mapping sensors to capture geospatial datawithin an observation window of the agricultural field. In oneembodiment, the observation window can be dimensioned such that a widthand length of the observation window are at least three times a nominalwidth of the annual crop rows. The deployed one or more aerial mappingsensors can be used to measure the actual annual crop row positionswithin the starting point observation window. The actual annual crop rowpositions can be used to create a geospatial data map of the entireagricultural field by plotting the actual annual crop row positions in aportion of the geospatial data map that corresponds to the startingpoint observation window, and filling in a remainder of the geospatialdata map with anticipated annual crop row positions corresponding to theannual crop rows outside of the starting point observation window. Inone embodiment, the measured actual annual crop row positions can beused to predict the anticipated annual crop row positions within theperimeter of the agricultural field. The self-direction program can beactivated to autonomously navigate the unmanned agricultural robotwithin the perimeter of the agricultural field.

In one embodiment, as the observation window moves along with theunmanned agricultural robot through the agricultural field, thegeospatial data map can be refined by replacing the anticipated annualcrop row positions with measured actual annual crop row positions. Inanother embodiment, the one or more aerial mapping sensors canperiodically refine the geospatial data map by replacing the anticipatedannual crop row positions with measured actual annual crop row positionswithin the observation window when an unexpected obstacle is encounteredduring autonomous navigation.

One embodiment of the present disclosure provides for an unmannedagricultural robot mapping system that can generate detailed maps of thegeospatial location of crop rows as the unmanned agricultural robotmoves through the agricultural field. The unmanned agricultural robotmapping system can include one or more unmanned agricultural robots,each having a power source and wheels or tracks or a combination thereoffor mobility. Each unmanned agricultural robot can include a mast thatextends vertically and can optionally be retracted when not in use. Themast can be used to elevate an aerial mapping sensor module, which caninclude one or more aerial mapping sensors used to determine thelocation of an unmanned agricultural robot relative to those crop rowsin its surroundings. In one embodiment, the aerial mapping sensor is adigital camera. In one embodiment, the aerial mapping sensor can beconfigured to determine distances to objects, at can comprise a stereocamera and/or light detection and ranging (LIDAR) sensor.

One embodiment of the present disclosure provides for an unmannedagricultural robot mapping system that includes an aerial mapping sensoron an extendable mast combined with one or more computer algorithms thatprocess the sensor data in order to resolve the geospatial location ofcrop rows in the local surroundings of the autonomous ground robot.

One embodiment of the present disclosure provides for the unmannedagricultural robot mapping system to be used for periodically resolvingthe location of crop rows in the unmanned agricultural robot's localsurroundings (local field map or L-FMAP) for the purpose of building afield map (FMAP) in real time. When a new L-FMAP is created, somefraction of the overall FMAP is known; the location of some rows may beregarded as anticipatory or tentative. Each new L-FMAP would extend theproportion of area, or row segments, with known locations.

In typical operation, the unmanned agricultural robot could begin at aknown location on the FMAP and use on-board navigational sensors, suchas LIDAR or stereocamera, to navigate between rows until a non-standardvegetation state is detected ahead, such as a patch of heavy weed growthor crop rows planted substantially perpendicular to the rows in whichthe unmanned agricultural robot is operating. In these cases, a newL-FMAP could be created thereby improving and updating the row layout onthe overall FMAP. In one embodiment of the present disclosure theunmanned agricultural robot could use onboard algorithms to determinethe best route to be taken given the now-improved FMAP. In anotherembodiment, the unmanned agricultural robot can send data including theFMAP and the robot's position and heading relative to the FMAP to aremotely-located human operator, who can make a determination of thebest path for the unmanned agricultural robot to take next. In such acase, the onboard systems of the unmanned agricultural robot can createseveral alternative paths to resolve the navigational impediment, andthe remotely-located operator may then select one path from the severalalternatives.

One embodiment of the present disclosure provides for the use of L-FMAPsin cases where an unexpected obstacle is encountered and inhibits theplanned motion of the unmanned agricultural robot. In such a case, theL-FMAP combined with the FMAP can provide one or more alternativecourses for the unmanned agricultural robot in order to avoid acollision with the obstacle. The unmanned agricultural robot can thenselect the appropriate course, or a remotely-located operator could beinvolved in selecting the course for the unmanned agricultural robot totake to avoid the obstacle. In a situation where the unmannedagricultural robot is operating with many rows of crop on either side ofit, a typical resolution involving an obstacle is to alter course to theright (or the left) and then continue parallel to the original directionuntil clear of the obstacle at which point returning to the left (or theright) until meeting and rejoining the original course. During thecourse of such a maneuver, the unmanned agricultural robot wouldnecessarily drive over some of the crop in order to avoid the obstacle,and the L-FMAP would be useful in limiting crop damage during theobstacle avoidance maneuver.

The summary above is not intended to describe each illustratedembodiment or every implementation of the present disclosure. Thefigures and the detailed description that follow more particularlyexemplify these embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure can be more completely understood in consideration of thefollowing detailed description of various embodiments of the disclosure,in connection with the accompanying drawings, in which:

FIG. 1 is a perspective view depicting an unmanned agricultural robotwith extendable mast configured with an aerial mapping sensor, inaccordance with an embodiment of the disclosure.

FIG. 2 is a top view depicting an observation window surrounding anautonomous agricultural robot operating between annual crop rows, inaccordance with an embodiment of the disclosure.

FIG. 3 depicts a geospatial data map depicting a perimeter of anagricultural field and an observation window at a starting point withinan agricultural field, in accordance with an embodiment of thedisclosure.

FIG. 4 depicts the geospatial data map of FIG. 3 with crop row positionsplotted on the geospatial data map, wherein actual crop row positionsare indicated by solid lines and anticipated crop row positions areindicated by dashed lines.

FIG. 5 depicts the geospatial data map of FIG. 4, wherein theobservation window has moved, and some of the previously plottedanticipated crop row positions have been replaced by newly verifiedactual crop row positions.

FIG. 6 depicts the geospatial data map of FIG. 5, wherein theobservation window has moved to a curve at the corner of theagricultural field.

FIG. 7 depicts the geospatial data map of FIG. 6, wherein theobservation window has moved further through the curve at the corner ofthe agricultural field.

FIG. 8 depicts the geospatial data map of FIG. 7, wherein newly measuredcrop row positions have been plotted as actual crop row positions.

FIG. 9 depicts the geospatial data map of FIG. 8, wherein theobservation window has moved to another curve at a corner of theagricultural field.

FIG. 10 depicts the geospatial data map of FIG. 9, wherein theobservation window has moved further through the curve at a corner ofthe agricultural field.

FIG. 11 depicts the geospatial data map of FIG. 10, with additionalnewly measured crop row positions plotted.

FIG. 12 depicts the geospatial data map of FIG. 11, with the actual croprow positions plotted as a path in proximity to a perimeter of theagricultural field.

FIG. 13 depicts a partial geospatial data map depicting anticipated croprow positions (indicated by dashed lines), as well as an area of heavyweed growth (hashed area), in accordance with an embodiment of thedisclosure.

FIG. 14 depicts the partial geospatial data map of FIG. 13, wherein theactual crop row positions (indicated by solid lines) surrounding theheavy weed growth (hashed area) within an observation window have beenmeasured, while the remaining rows in the partial geospatial data mapoutside of the observation window remain as anticipated crop rowpositions (indicated by dashed lines).

FIG. 15 depicts a partial geospatial data map depicting an area withpartially overlapping crop rows (hashed area), in accordance with anembodiment of the disclosure.

FIG. 16 depicts a partial geospatial data map, wherein plotted actualcrop row positions (indicated by solid lines) include end segments(indicated by heavy dashed lines) that are known to be planted in errorin that they overlap actual crop rows oriented in a substantiallyperpendicular direction.

FIG. 17 depicts a partial geospatial data map depicting an obstacle(hashed area), such as a rock, located between anticipated crop rowpositions (indicated by dashed lines), in accordance with an embodimentof the disclosure.

FIG. 18 depicts the partial geospatial data map of FIG. 17, wherein theactual crop row positions (indicated by solid lines) surrounding theobstacle (hashed area) within an observation window have been measured,while the remaining rows in the partial geospatial data map outside ofthe observation window remain as anticipated crop row positions(indicated by dashed lines).

While embodiments of the disclosure are amenable to variousmodifications and alternative forms, specifics thereof are shown by wayof example in the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the disclosureto the particular embodiments described. On the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the disclosure as defined by the appendedclaims.

DETAILED DESCRIPTION

Referring to FIG. 1, an unmanned agricultural robot 100 is depictedaccording to an embodiment of the disclosure. Unmanned agriculturalrobot 100 can be configured to selectively perform one or more in seasonmanagement tasks on an agricultural field 101 having adjacent rows ofannual crop rows 102 planted so as to provide a conventional annual croprow spacing between two adjacent annual crop rows 102 of not more thanthirty-six inches. Unmanned agricultural robot 100 can include a base104 operably coupled to a plurality of ground engaging wheels 106.Unmanned agricultural robot 100 can have a first lateral side 108A and asecond lateral side 108B, wherein the first and second lateral sides108A/B oppose one another and are separated by a distance defining thewidth of the unmanned agricultural robot 100, the width so dimensionedas to be receivable within the space between two adjacent annual croprows 102. Unmanned agricultural robot 100 can be programmed with aself-direction program to autonomously navigate the unmannedagricultural robot 100 between the two adjacent annual crop rows 102,and to avoid other unmanned agricultural robots, while selectivelyperforming the one or more in season management tasks on theagricultural field 101. In some embodiments, unmanned agricultural robot100 can include a first portion 110, and a second portion 112, whereinthe first portion 110 is pivotably coupled to the second portion 112,and wherein each portion includes at least one ground engaging wheel106.

In one embodiment, unmanned agricultural robot 100 includes a mast 114.Mast 114 can be extendable and telescoping, so as to be selectivelyraised and lowered from the base 104 of unmanned agricultural robot 100.One or more aerial mapping sensors 116 can be operably coupled to a topportion of the mast 114. In one embodiment, mast 114 can extendvertically above the unmanned agricultural robot 100, thereby deployingthe one or more aerial mapping sensors 116 at a height above the annualcrop rows 102, so as to enable the one or more aerial mapping sensors116 to capture geospatial data within an observation window of theagricultural field 101.

Aerial mapping sensors 116 can be capable of providing data that can beused to localize the position of the unmanned agricultural robot 100 inrelation to the annual crop rows 102 within a given observation window.Aerial mapping sensors 116 can include a standard imaging camera, astereo camera or a laser rangefinder (LIDAR), or a combination thereofwhich can provide data useful for determining distances to objects.

Referring to FIG. 2, an agricultural field 101 having annual crop rows102 is depicted. Unmanned agricultural robot 100 is positioned withinagricultural field 101 with one or more aerial sensors 116 deployed at aheight above the annual crop rows 102 so as to enable the one or moreaerial mapping sensors 116 to capture geospatial data within anobservation window 118. In one embodiment, observation window 118 has alength of at least fifty feet and a width of at least fifty feet. Inother embodiments, the size of the observation window 118 depends uponthe height of the one or more aerial mapping sensors 116 above theagricultural field 101. In one embodiment, the observation window 118can be dimensioned such that width and length are at least equivalent toseveral times the nominal annual crop row 102 spacing. For example, inone embodiment, the observation window 118 has a width and length thatis at least three times the nominal width of the annual crop rows 102.In another embodiment, the observation window 118 has a width so as topermit the one or more aerial mapping sensors 116 to view between fourand ten adjacent crop rows, and a length of a substantially similardimension. Unmanned agricultural robot 100 can use the one or moreaerial mapping sensors 116 to measure the actual annual crop rowpositions 120 within the observation window 118 for the generation of ageospatial data map 122.

Referring to FIG. 3, a geospatial data map 122 is depicted in accordancewith an embodiment of the disclosure. The geospatial data map 122 of theentire agricultural field 101 is generated by plotting the actual andanticipated annual crop row positions within a perimeter 124 of theagricultural field 101. The perimeter 124 of an agricultural field 101is generally known and can be predefined, but the precise layout of theannual crop rows 102 is often not known with a high degree of precision.

FIG. 3 depicts the unmanned agricultural robot 100 at a starting point.The one or more aerial mapping sensors 116 can be used to measure theactual annual crop row positions 120 within the starting pointobservation window 118. One or more computer algorithms in combinationwith the one or more aerial mapping sensors 116 can be used to determinethe exact geospatial location of the annual crop rows 102 relative tothe unmanned agricultural robot 100. The actual crop row positions 120can then be plotted on the geospatial data map 122, which as depicted inFIG. 3, can be plotted as one or more solid lines.

Referring to FIG. 4, plotting of the annual crop row positions can beextended on geospatial data map 122 by continuing to plot theanticipated annual crop row positions 126, corresponding to the annualcrop rows 102 outside of the starting point of the observation window118. The anticipated annual crop row positions 126 can be plotted ongeospatial data map 122 as one or more dashed or dotted lines. In otherembodiments, the various annual crop row positions 120 and anticipatedannual crop row positions 126 are represented by other figures,including one or more continuous and/or noncontiguous symbols orrepresentations. In embodiments where the geospatial data map 122 isstored electronically without visual display on a user interface, theanticipated annual crop row positions 126 and the actual crop rowpositions 120 can be represented by mathematical or other datarepresentations.

In one embodiment, actual crop row positions 120 can be extendedlongitudinally as anticipated annual crop row positions 126. Additionalanticipated crop row positions 126 can be added laterally to either sideof the actual crop row positions 120 within the perimeter 124.Accordingly, in one embodiment, the actual annual crop row positions 120are used to predict the anticipated annual crop row positions 126 withinthe perimeter 124 of agricultural field 101.

Referring to FIG. 5, the self-direction program of the unmannedagricultural robot 100 can be activated to autonomously navigate theunmanned agricultural robot 100 within the perimeter 124 of theagricultural field 101, while selectively performing one or more inseason management tasks. As the unmanned agricultural robot 100 movesthrough the agricultural field 101, onboard navigational sensors guidethe machine between both actual crop row positions 120 (depicted assolid lines) and anticipated crop row positions 126 (depicted as dashedlines) until such a point that the layout of actual crop rows 120 orother vegetation create significant uncertainty about the correct pathfor the agricultural robot 100 to follow. At this point, the aerialmapping sensor 116 can be deployed and a local field map (L-FMAP)determined within the observation window 118, creating additional actualcrop row positions 120 for reference.

In one embodiment, as the unmanned agricultural robot 100 advancesbetween anticipated crop row positions 126 without encounteringunexpected vegetation, some portion of the anticipated crop rowpositions 126 are re-labeled as actual crop rows 120.

Referring to FIGS. 6-12, a nonstandard annual crop row layout may existat curved rows at the corners of the agricultural field 101. In thesesituations, the one or more aerial mapping sensors 116 can be used tocapture the actual crop row positions 120, and can be used to refinegeospatial data map 122. When progressing through curved rows, themeasured, actual crop row positions 120 can be used to predictanticipated annual crop row positions 126, particularly when approachingthe perimeter 124 of the agricultural field 101. For example, given aknown location of the perimeter 124 of the agricultural field, theactual crop row positions 120 can be used to extend a pattern ofanticipated annual crop row positions 126 to update and further refinethe geospatial data map 122.

Referring to FIGS. 13-14, a partial depiction of a geospatial data map122 in which anticipated annual crop row positions 126 are plotted(depicted by dashed lines) may include one or more areas of heavy weedgrowth 128. The area of heavy weed growth 128 may not be initiallyplotted on the geospatial data map 122. In other embodiments, knownareas of heavy weed growth 128 can be initially plotted on geospatialdata map 122, and refined by measurement from aerial mapping sensors 116at a later time. For example, observation window 118 can be moved alongwith unmanned agricultural robot 100 to the area of agricultural field101 containing the area of heavy weed growth 128. The actual position ofarea of heavy weed growth 128, along with its dimensions, density, andthe surrounding actual crop row positions 120 can be measured by the oneor more aerial mapping sensors 116. The area of heavy weed growth 128and the surrounding actual crop row positions 120 can then be plotted ongeospatial data map 122, as depicted in FIG. 14. In such a scenario, theunmanned agricultural robot 100 can then continue moving forward throughthe area of heavy weed growth 128 with a high degree of certainty thatit is running over weeds and not through annual crop rows 102. Inanother scenario, the unmanned agricultural robot 100 couldsimultaneously take action to remediate the identified weed growth as itcontinues to carry out the main in season management task.

Referring to FIGS. 15-16, a partial depiction of a geospatial data map122 in which an area with partially overlapping annual crop rows 130 isdepicted. Areas of overlapping annual crop rows 130 most commonly occurnear the perimeter 124 of the agricultural field 101. The area withpartially overlapping annual crop rows 130 may not be initially plottedon the geospatial data map 122. In other embodiments, known areas ofpartially overlapping annual crop rows 130 can be initially plotted onthe geospatial data map 122, and refined by measurement from aerialmapping sensors 116 at a later time. For example, observation window 118can be moved along with unmanned agricultural robot 100 to the area ofthe agricultural field 101 containing the area with partiallyoverlapping annual crop rows 130. The actual position of the area withpartially overlapping crop rows 130 can be measured by the one or moreaerial mapping sensors 116, wherein the area with partially overlappingannual crop rows 130 can be comprised of actual crop row positions 120that can be plotted on the geospatial data map 122.

As depicted in FIG. 16, in one embodiment, the unmanned agriculturalrobot 100 can identify some of the actual crop row positions 120 ashaving been mis-planted row segments 132. The mis-planted row segments132 can then be plotted on geospatial data map 122. The unmannedagricultural robot can then continue to operate within the area withpartially overlapping annual crop rows 130, being careful to avoid thecorrectly planted actual crop row positions 120, but treating themis-planted row segments 132 as crop plants that can be driven over asneeded.

Referring to FIGS. 17-18, a partial depiction of the geospatial data map122 in which anticipated annual crop row positions 126 are plotted(depicted by dashed lines) may include one or more obstacles 134. Anobstacle 134 may be initially plotted on the geospatial data map 122,particularly where an obstacle 134 is known. In other embodiments,obstacles 134 may not be recognized until one or more onboard cameras onthe unmanned agricultural robot 100 have detected an obstacle 134 in theintended path of the unmanned agricultural robot 100 during operation.In this situation, the mast 114 could be deployed to raise the aerialmapping sensor 116 above obstacle 134 to obtain a bird's eye view of theobstacle 134. The actual position of the obstacle 134, along with thesurrounding actual crop row positions 120 can then be plotted ongeospatial data map 122, as depicted in FIG. 18.

It should be understood that the individual steps used in the methods ofthe present teachings may be performed in any order and/orsimultaneously, as long as the teaching remains operable. Furthermore,it should be understood that the apparatus and methods of the presentteachings can include any number, or all, of the described embodiments,as long as the teaching remains operable.

Persons of ordinary skill in the relevant arts will recognize thatembodiments may comprise fewer features than illustrated in anyindividual embodiment described above. The embodiments described hereinare not meant to be an exhaustive presentation of the ways in which thevarious features may be combined. Accordingly, the embodiments are notmutually exclusive combinations of features; rather, embodiments cancomprise a combination of different individual features selected fromdifferent individual embodiments, as understood by persons of ordinaryskill in the art. Moreover, elements described with respect to oneembodiment can be implemented in other embodiments even when notdescribed in such embodiments unless otherwise noted. Although adependent claim may refer in the claims to a specific combination withone or more other claims, other embodiments can also include acombination of the dependent claim with the subject matter of each otherdependent claim or a combination of one or more features with otherdependent or independent claims. Such combinations are proposed hereinunless it is stated that a specific combination is not intended.Furthermore, it is intended also to include features of a claim in anyother independent claim even if this claim is not directly madedependent to the independent claim.

Moreover, reference in the specification to “one embodiment,” “anembodiment,” or “some embodiments” means that a particular feature,structure, or characteristic, described in connection with theembodiment, is included in at least one embodiment of the teaching. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment.

Any incorporation by reference of documents above is limited such thatno subject matter is incorporated that is contrary to the explicitdisclosure herein. Any incorporation by reference of documents above isfurther limited such that no claims included in the documents areincorporated by reference herein. Any incorporation by reference ofdocuments above is yet further limited such that any definitionsprovided in the documents are not incorporated by reference hereinunless expressly included herein. For purposes of interpreting theclaims, it is expressly intended that the provisions of Section 112,sixth paragraph of 35 U.S.C. are not to be invoked unless the specificterms “means for” or “step for” are recited in a claim.

What is claimed is:
 1. A method of using an unmanned agricultural robotto generate an anticipatory geospatial data map of positions of annualcrop rows planted within a perimeter of an agricultural field, themethod comprising: delivering the unmanned agricultural robot to theagricultural field, wherein the unmanned agricultural robot isprogrammed with a self-direction program; positioning the unmannedagricultural robot at a starting point on the agricultural field;deploying one or more aerial mapping sensors at a height above theagricultural field, so as to enable the one or more aerial mappingsensors to capture geospatial data within an observation window of theagricultural field, the observation window dimensioned such that a widthand length of the observation window are at least three times a nominalwidth of the annual crop rows; using the one or more aerial mappingsensors to measure the actual annual crop row positions within thestarting point observation window relative to at least one of theunmanned agricultural robot and the perimeter of the agricultural field;creating the geospatial data map of the entire agricultural field byplotting the actual annual crop row positions in a portion of thegeospatial data map that corresponds to the starting point observationwindow, and filling in a remainder of the geospatial data map withanticipated annual crop row positions corresponding to the annual croprows outside of the starting point observation window, wherein themeasured actual annual crop row positions are used to predict theanticipated annual crop row positions within the perimeter of theagricultural field; activating the self-direction program toautonomously navigate the unmanned agricultural robot within theperimeter of the agricultural field; and using the one or more aerialmapping sensors to periodically refine the geospatial data map byreplacing the anticipated annual crop row positions with measured actualannual crop row positions within the observation window when anunexpected obstacle is encountered during autonomous navigation.